LLMs are eroding my software engineering career and I don't know what to do (opens in new tab)

I always remember of the infamous Steve Jobs quote "Ideas are cheap". If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness".

What I think is often overlooked is the human "Willingness" and "Care" of staying with the thing for the lack of a better term. What I mean by that is that a lot of people just don't care enough, or don't want to, build, maintain, and own things. Sure you can ship V1 faster, but will you remain on the grind?

I think a great example of what probably will happen is found in Suno, the AI Music thing. I don't know if y'all have tried it, but it now produces really good stuff. What's happening there? A lot of people play with their own little universe and get tired quickly, move away from it, and only a few prolific creators stay and turn it into a "job like" environment.

We may have shifted the scale and the economics of "delegation" and "execution" but I think there are still a lot of other factors to consider.

Wut? I pilot LLMs all day but there's no way in hell I'd agree to be at the helm of a finance product. That first pillar is still there. Maybe the author isn't aware of the impact they have, but I know, with the evidence of reverted PRs, that when I step outside my area of deep knowledge I can no longer call BS on the agents. Our most capable agent, with access to the same kind of distributed systems the author talks about, is regularly wrong, frequently myopic, and just outright dumb constantly. It's the expertise of engineers on the team that push it back on track.

> The company is now hiring again for a few roles and domain familiarity is not a strong differentiator anymore. We used to list "Software Engineer - Area". Now it's just "Software Engineer" and the team assignment comes after the offer is accepted.

> Of course, this is good for brilliant engineers that never had the chance to get deep into the domain and now have better chances at getting a job, but it's also sad to think that other brilliant engineers that spent their lives collecting domain knowledge are now competing on the same lane.

If the author's vision of the future is correct, then competent software engineers are safe. Domain knowledge can be learnt much quicker than how to apply good engineering principles.

Engineers whose main competitive advantage is domain knowledge are probably not that brilliant at engineering. They might still find employment in other areas of the industry where they accumulated domain knowledge.

I see many comments saying, "AI can't do X with 80-100% accuracy; therefore our professions are in good hands."

While I don't want to sound overly pessimistic, the models are improving at a rapid rate. If asked ~3 years ago where the state of the models are today, it would sound like sci-fi if answered, "the models are creating full MVP apps in ~30 minutes with one prompt".

The hurdles the models are facing now, like reducing hallucination rates, ensuring compliance, and keeping a clean codebase, do not seem far away from being resolved IMO. Fetching specific information is already partially done with various MCP servers / RAG.

I am, of course, a bit worried about the future of software engineers. If these quirks are resolved, where do their professions fit in the industry? Delegating tasks to the AI model? Unfortunately, this does not require years of expertise, which is a double-edged sword. Reviewing AI's output? Ask it to explain each line not understood.

I think we will see more waves of larger layoffs, similar to how human computers were replaced by digital computers. To some, doing complex mathematical calculations mentally is a fun task / challenge, but it is ultimately significantly slower and more error-prone than calculating with a computer. In the same way, I think hand-crafting code will be seen as a fun "challenge" and AI will be seen as the "modern-day calculator".

I spent most of the last two years making a rather large hobby project. A 3D renderer for cad/visualization. It's pretty hard and slow work because getting anything wrong is usually hard to debug. Get a sign wrong and you have a black image suddenly, with painstaking debugging following. I worked on it for evenings and weekends, probably totaling a man-month of work or so.

As a fun experiment, yesterday I asked a model to create this for me. It almost one shotted it. After a couple of iterations and 30 minutes I had made what I made over two years. The total AI cost (deepseek api, so entirely usage based) was $0.5.

Now, I didn't enjoy making this AI guided version. And I didn't learn anything. But terrifyingly it has removed the drive to make another 2 year project "by hand". The end result (the runnihg demo) was never the goal. But still I can't now make myself hand-craft what an AI can spit out in an hour for $1!

This is what bothers me the most. I'm old and senior enough that I don't fear for my job. But the AI thing just swallowed my hobby.

To me looks like, if we're not collectively careful, civilisation will soon be on a path to an evolutionary dead-end.

Anything that can replace a deeply experienced s/ware engineer can replace anyone in the employment stack, meaning that only the owners of capital will be left, and they too will soon fade as the economy falls off a cliff and money has no value, because the only value that money has is the value of a human backing that, with thought, with ideas, with human output.

Whether you like it or not, "Economic output" is just a different phrase for "Human output that is valuable". When all human output is valued at the fractions of a penny per month of work, there is no future.

My career path is suprisingly similar to the author's. Weirdly enough, what he takes as the first pillar to fall is the one I see most undamaged currently.

LLMs routinely fail at our business specifics: Local tax regulations, particularities of the accounting process, specifics of our ledger implementations. They're great at refactoring, translating between languages, tracing bugs on existing code even, but there is always many things subtly wrong iterating and expanding our domain.

This might be because the companies I worked for happen to be tackling complex domains precisely for moat-building reasons. They stay in business explicitly because there's not a book out there you can read to build a clone, the knowhow stays inside.

Also, a fintech whose managers recommend speeding up design docs with AI sounds way too careless to be in the money handling business. It's way, way too easy to end up with millions incorrectly allocated, particularly if you deal with high volumes of small transactions. These bugs are always a bitch to deal with because correcting the logic is just step one, you then have to correct all the wrongly calculated data in immutable DBs, move around the red tape and client comms, and your fix is bound to become a gotcha that new features and observability have to take into account ("remember that there's a bump in the data in february 2 because we had incident X".)

> I don't know what to do.

Ride the wave. You rode it when websites/webapps were the wave. I came into software industry before internet, kept changing my horse. You are never too old to learn new tricks. The new wave create new kind of work and workers. Be one of them. Ride the beast, master the tools. It's the same game again.

> Maybe I should consider transforming my woodworking hobby into a profession...

Whatever your feelings on the future of the industry are, it's hard to imagine you'll find more professional success in artisan woodworking than artisan software.

I sympathise with the author being in the same boat, largely.

I just want to emphasise a point... Calculators give 100% correct answers and yet we still hire accountants; for the simple fact that we don't want all to be accountants.

People will hire software engineers for the simple fact that they do not want to be software engineers.

This doesn’t read to me as someone who is sincerely impressed or rather surprised what ai is capable off.

This reads like someone is trying to convince me, that ai is just this good, and that the author is telling me to use more ai.

To me this sounds like: Trust me, it’s really bad, i know what I’m talking about. Just lean into it, or change profession.

The last sentence in the article is correct:

"Maybe I should consider transforming my woodworking hobby into a profession."

As an AI optimist, I think all forced labor should eventually be done by AI. People can then spend their time pursuing their own hobbies. Just as many people still play Go after AlphaGo appeared, because they genuinely love the game.

In the future, coding may return to being an art form. People will no longer focus on utility alone, but instead on the enjoyment of the process of writing code itself.

I've posted this before but worth posting again:

I work in DevOps at a firm that has been very enthusiastic about using LLMs (in the good sense).

The phases were basically:

- try out having the LLM do "a lot"

- now even more

- now run multiple agents

- back to single agents but have the agents build tools

- tools that are deterministic AND usable by both the humans (EDIT: and the LLMs)

The reasons:

1. Deterministic tools (for both deployments and testing) get you a binary answer and it's repeatable

2. In the event of an outage, you can always fall back to the tool that a human can run

3. It's faster. A quick script can run in <30 seconds but "confabulating" always seemed to take 2-3 minutes.

Really, we are back to this article: https://spawn-queue.acm.org/doi/10.1145/3194653.3197520 aka "make a list of tasks, write scripts for each task, combine the scripts into functions, functions become a system"

-- END of original post --

What I would add:

if you let LLMs do whatever they want, they will happily make code. You can add tests to confirm that the tests work (which you used to do with human code, right?). You can also read the code.

When you read the code, you'll find that they sometimes do totally bananas things that still produce working code (I've seen humans do this too but that's another story).

In other words, you still need to make sure the system being built makes sense.

More succinctly:

Coding may be dead but software engineering is alive and kicking.

I’ve been using Claude Code with Opus 4.7; it’s not that the code it produces is wrong, it simply tends to write too much of it. In my opinion it’s still worth thinking about a particular feature and finding the best way to fit it into your code because Claude will often just pick a layer of the stack (maybe presentation), and jam it in there. A couple weeks later you need this data somewhere else and Claude can’t reuse the code (maybe in the service layer) so it kind of “ports” it over. Unless a person is paying attention we now have the double the amount of code and duplicate logic. I don’t see AI tools like Claude getting better at this anytime soon.

Where I work there’s already pressure to use Opus 4.7 less to save money, someone mentioned using a smaller model for “simple bug fixes”. This might work sometimes but how often do we really know it’s a simple bug fixe ahead of time? I suspect as costs go up we’ll see interest in using these tools to write “all the code” go down. As people migrate to cheaper and less effective models I suspect we’ll see the pressure to skip reviewing that code dissipate as well.

We’ll see where we land, maybe it won’t as dramatically different as the author of this post fears.

> I spent 10 years (even more when you account for non-profession experience) getting good at things that are becoming less and less valuable.

This is just how it is, and has always been in this industry. And it takes about 10 years to realize it.

When I started my career in software, businesses were still writing new code in COBOL. 10 years later those skills were pretty much useless, except for dwindling maintenance roles.

Then there was the client/server era. Then the web era. Then mobile. Then cloud, etc.

All the same functionality, written and re-written time and time again, using the latest popular stacks and methodologies.

I hope to be retiring in a few years and pretty much everything I have learned over nearly 40 years is no longer applicable or is at best losing relevancy to the way sofware is built today. And that's how it's always been.

There are lots of positives that have resulted from using AI in software engineering. (1) No more long repetitive text editing sessions. I.e. changing namespaces or replacing deprecated APIs with the "correct" ones. AI will make nearly perfect text modifications with ease. (2) No more bike-shedding code reviewers nitpicking over every tiny coding decision. I.e, "you should use std::format instead of std::stringstream". AI will match the existing set of nitpicks so you don't have to. (3) Average Joe's and Jane's can craft applications by just talking to the computer. This might inject a freshness to the current state of software. Currently, we are all forced to use the same bloated applications like Word, Excel, Jira, and Photoshop. We are currently forced to deal with the same set of monopolistic SW companies. Now average folk can solve problems and avoid dealing with Microsoft for a spreadsheet program.

It's odd to me how quickly the author devalues their own experience just because AI can do certain things well. There's a huge chasm between what AI can do when prompted by an expert software engineer vs a non-technical person. Sure the models and the tooling will get better, but it still needs to be driven by someone with an intuition for how software works and able to dig in when necessary to unpack and correct the hallucinations, misplaced assumptions, or straight up borked code that will come from the gap between what a human wants and what they can express in words.

I have no idea how things will play out, but so far I am not worried because the amount of software continues to increase, and AI only accelerates that trend. This will require the same mental modeling, first principles thinking, and relentless curiosity that already formed the foundation of the software engineer skillset.

This feels fake/engineered but regardless of that also redundant.

It's the exact same story that we've heard countless times by now. Hosted on a blog with just a single post. Named in a way that suggests that said blog was created for this very single post.

What is there to learn from this other than LLMs seem to be bad for some people's psyches and that AI companies need these very stories to not get their funding shut down?

Maybe denial is a river in Egypt, but I (most of the time) believe that the glass is half full and we will need MORE humans as LLMs gain in ability. My thesis:

1. most jobs are created by small to medium size enterprise

2. the throttle for new SMEs has been people, money and ideas, in that order

3. with LLMs being a force multiplier, fewer technical people are needed but some people still ARE needed

4. with less throttling, MORE SMEs will be created with more jobs - they will be able to do more, faster, but still need some human oversight.

Also, what is the point of software if it is not to serve human needs?

Also, in open source, community building and tending is a very human enterprise that will not be replaced by bots any time soon. So, as coding becomes commoditised, perhaps the soft skills backed by technical knowledge will be the complementary skill that increases in value.

Or, maybe it's time for me to become an itinerant folk musician.

I've consulted with some big companies on AI strategy. I tell them there are two approaches to AI.

1) Train AI to replace human work. This gives you 50% quality for 10% cost. 2) Train AI to assist human workers. This gives you 200% quality for 110% cost.

Most companies will go with option 1, and it's a race to the bottom. Eventually, someone will go with option 2 and gather up all of the pieces and take over the market.

Code quality and architecture still matter, because they also make it easier for LLMs to reason about the system.

That said, Opus 4.8 and Codex 5.5 both can write code that is higher quality than your average engineer. They are not quite there yet in terms of code re-use, but I think that's a solvable problem.

If corporations really thought LLMs were a great cost-savings tool, then the obvious target for replacement are not the lower-paid staff, but the higher-paid staff - the ‘product managers and stakeholders’. That justifies token burn, replacing the 7- and 8-figure people, right?

But that’s not the real goal, is it? The goal is to inflate the stock value, take the cream off the top, and dump the whole business on the pension funds, maybe creating a too-big-to-fail scenario where the government steps in an bails out the industry as with the airlines during Covid.

This is why all the testimonials and narratives are so suspect - nobody knows what fraction of online posts were created simply to sell the narrative that LLMs are this incredible disruptive tool that will change the world, solely in order to create FOMO in the investor class.

In this particular case, I’d like to see links to samples of LLM created codebases for “PCI compliance, double-entry ledgers, escrows, reconciliation, payment lifecycles, bank transfer idempotency”. It should be easy to put an open-source LLM-generated version up on github, right? And if not, why not?

There’s one force where software engineering is being automated by LLMs, but the other force is that there isn’t really much more software that needs to be built. Even before AI coding became big, back in 2021, we were already in late stage SaaS territory where each new idea was an increasingly minor variation of an existing idea. There were no new GitHubs, Herokus, Stripes, Salesforces, Instagrams, Reddits, just variations of those for more specialized markets.

It’s really unfortunate that AI hasn’t raised the ceiling on the space of possibilities as much as it’s raised the floor on how much can be automated, we’re all getting squeezed in the space between.

> And then I started realizing: all the knowledge I have accumulated over the years: the trade-offs between implementations, how acquiring works, how to structure idempotency to prevent double-charges, everything, was becoming useless.

How is that true? I've been using Opus on an industry scale over last 6 months and this is just not real.

It has consistently with a certain percentage of chance each time (and no claude.md and skills do not stop it fully):

* Suggested to remove tests to allow for things to pass

* Suggested remove an error so that things can be "unblocked"

* Suggested to use a second path when the original path ran into problem instead of making the original path accomodate for that possibility.

* Suggested or silently added "features" or "guardrail" that I don't want.

* Can be left unsupervised only if given a goal that it can verify against itself. Without such clear goal (e.g. this test in the integration environment must be fixed), it flounders.

I'm not using just the native harness (e.g. CC) either, with additional, customized harness, the behavior improves somewhat but are still fundamentally constrained and cannot really be trusted without verification.

See my methodology (100% handwritten): https://aperocky.com/blog/post.html?slug=agentic-development....

Being a heavy user I think I've ran into every single hallucination that the model can do over development release and operations. I am still a heavy user but there are a lot of value in recognizing where exactly LLM's limit is and work around that.

I have 25 years of experience with a single programming language that’s still in broad modern use. My employer recently moved me to a project that doesn’t use that language. The new language is completely foreign and hard for me to read. BUT we are supposed to maximize our AI usage. I made a few agents and skills to automate workflows, and now I can just say “$do-ticket ABC-1234”, and it gets done using TDD and hands me a report on what changes it made and why, along with an open Pull Request that automatically gets reviewed (again) by Codex. And the code works the first time 98% of the time. I don’t know the language, and it seems like people would prefer that I not know it, because then my AI use is necessarily maximal, but what am I even doing? Just telling AI to do a ticket? I see developer salaries trending toward minimum wage in the near future, and it’s scary, so I’m learning and leveraging LLM stuff, but even that will only help for so long. It’s a crazy time in our industry.

There's a certain irony in masters of automation lamenting that their roles are being automated. I wonder whether the jobs their efforts eroded in the past ever got the same thoughts...

Programming, logic, etc are skills and toolkits. The optimal state of society is everybody being able to apply them, not just the enlightened compsci caste. There was a time in the past where scribes were paid nice cash for their efforts, too.

I guess the lesson to learn here is treating a toolkit as an identity and job for life. By virturee of the essence of the job itself - if the tool gets cheaper and more widespread, it's aactually success, not betrayal.

Hey, I agree with you. I and my team have the same existential worries. This was one reason why I bought my own subscriptions to Claude and Codex to learn and skill up. I have been using that to intensively do side projects, curating prompts and workflows to see how these new tools fit in my toolbox. I have been bringing back the learnings to my team so that they can upskill as well.

For context, I use Claude and Codex (side projects, Max and Pro plans respectively) and Gemini at work.

The key takeaway I have is: These tools have let me climb up the value chain ladder.

Even with Claude (Max plan, Opus 4.8, High Effort), it makes tons of mistakes, assumes a lot, misses nuance and doesn't really think through every aspect of the problem from every angle. Limited memory, lack of full context and a lack of experience with real world distributed systems means that the initial solutions they offer need a lot of iteration and refinement. Just like any junior engineer would need to do.

So, you might feel that with a LLM, "this should just be an hour" but it usually becomes a 2-week exercise for me.

Which brings me to what I tell my team repeatedly: "the only person with the big picture is you." I ask them to focus on thinking, ideating, refining. Do quick PoCs, talk to customers, discern what they are trying to achive, and what you can do to solve those problems. Work with Gemini (we can only use Gemini at work) to iterate until you are comfortable with the full solution end to end with all the nuances. Then let the agent code.

This moves you up the value chain from being a programmer to a problem solver. That's what software engineering has always been about: solving problems. Don't be discouraged. In fact, I am having more fun, am more energized and loving my craft even more now with LLMs. I am able to write down my thoughts, iterate on them and create a one-pager for ideas fast and get them to my team for them to think about. Sure, probably half of them we discard because it's usually not a "now" problem but we put that in our backlog to dust off when the first customer asks for it.

I think this is the first time I saw someone describe so clearly my concerns and experience with LLMs.

I have little to add to it, except that I agree completely. Not sure what’s next

Interesting that this dev sees domain knowledge as the most important part of his job. Over my nearly 30 year history, I consider domain knowledge as the least important aspect, and in fact have experience of many varied domains. 3 years web development, 2 years systems administration, 5 years point-of-sale / payment systems, 3 years performance management software, 16 years games development, 2 years GPU development tools, etc.

In every case when I've shifted domains, the skills that have got me the job were demonstrable solid programming experience on a wide variety of systems, with only a tangential link to the new company's business. In each case, I've gone in knowing almost none of the domain knowledge, but it's never been a problem because the business analysts know that stuff and tell me what they want me to do, or it's been stuff I've been able to pick up in the first few months.

For example, when I switched to games development it was the combo of systems admin and web backend development that the company wanted, I actually used none of those skills in the first year doing what they hired me for, and pretty quickly I'd transitioned from that to become a rendering engineer, and I've now spent the majority of my career optimising shaders and game engines.

So for me, it's certainly the case that I value my adaptability across domains, and I'm not worried about having to shift to another business domain because I know I'll be able to produce whatever it is they want if there's a reasonable spec in place.

Sure, when hiring if you have 2 candidates - 1 with the exact domain knowledge you want, and 1 without, the one with domain knowledge has a head start, but in the case where nobody has that domain knowledge (or in the case of the article, it doesn't matter because AI levels the field), then I don't think it matters much. Personally, I'd rather be the person with the broadest skills and able to pick up what I need than to have been stuck doing the same thing my entire career.

There's no doubt that LLM increases our efficiency, be it producing prototypes, generating production code, debugging our systems, or more. So, if there's no more demand or reduced work hours, some of us will lose our jobs. I certainly hope the Jevon's Paradox kicks in faster, as in months instead of years, let alone decades.

If we don't consider the potential loss of our jobs, on the other hand, isn't it great that we don't have to repeatedly do what we already know how to do? I mean, how many times can we feel the thrill by writing the same CRUD applications? How many times do we have to design the same idempotent APIs? It's also a relief that we could spend way less time figuring out mitigations or root causes when there is a production incident.

This reminds me of the scribes before Gutenberg's moveable-type printing press. They spent their life in scriptoriums copying the manuscripts by hand. They earned three times of the average income of their times. They were highly skilled labor. It required years of training, deep literacy, and a high level of domain expertise. Yet, history showed that even highly specialized expertise can be mechanically reproduced.

That appears to be exactly what LLMs are doing for us: automating the digital equivalent of manual transcription, such as setting up the repetitive boilerplate, sketching out the standard APIs, finding predictable bug fixes.

I'm not sure about others, but I have to face the same existential question today: as software engineers, where does our true value lie? Is it merely in learning, memorizing, and, reproducing patterns that others have already built. More often than not, patterns that an LLM can now piece together better and faster? Or is it in taking everything we’ve learned and applying it to solve entirely new, messy, and uniquely human problems? If our worth is tied to how well we copy the past, we are already obsolete. Our value has to shift from being human repositories of known solutions to being creators who venture into the unknown.

It is, of course, easier said than done. Hence I have likely the same level of stress as other software engineers.

Based on what I've been seeing/reading online the past ~6 months or so, I think there's a self-fulfilling prophecy going on here.

Developers are concerned about jobs going away, but how often are they pushing back in their orgs about how AI works? In response to "are you using AI to move faster," how many are responding with "yes, but there are some things you should know..."?

If there's no pushback and just pure acceptance of stuff like tokenmaxxing, then what does anybody expect when the broader narrative around AI is that it can help a novice to grind out miracles (i.e., "holy crap, if this is what a novice can do, what can an expert do?!")?

Of course leadership is confused because (it seems) few are asserting expertise, saying "no," and stating a clear case as to why they're doing that.

The default excuse is "I don't want to lose my job" (which is a fair reaction to all of this, especially these days), but it's worth considering when/how that choice is actually just shooting future you in the foot later. It seems there's a broader trend toward compliance more than there is "you hired me to do this job properly, did you not?"

I posted this elsewhere, but I think it still has a valuable insight to bring to the table: https://halecraft.org/software-engineering-is-the-new-manufa...

> LLMs are regression-to-the-mean machines--they pull junior developers up, and drag senior developers down. Taming them requires trading the romance of 'code as craft' for the physics of manufacturing.

The thing I don't know is: how do we decide which direction is most valuable? I can see arguments in both directions--quality vs quantity, essentially. I think there's a strong argument for the value of both:

- we need more quantity of software: for a long time, the ability to write software has been locked up, confined to a closed cabal of specialists

- we need more quality in software: we depend more and more on software in every aspect of our lives, mistakes are intolerable and should be avoided

>Of course, I'm still employable because someone has to review the code and steer the robot...

We will work for the robots, steering them to steer us.

> "Now I have CLIs that one-shots bugs across distributed systems for me. Bugs that I couldn't solve in the past. Bugs that would take 2 days of full-time debugging. Bugs across distributed systems that lack distributed observability. 90% of the bugs are one-shotted now, including bizarre race conditions, unexpected corner-cases, third-party integration issues, undocumented API edge cases, everything. I hardly have to intervene."

The fact that the author can articulate _why_ the AI is getting so good is kind of a moat for specialist, right? Imagine a layman prompting without domain expertise:

"There is likely a race condition here + [long-winded explanation and analysis carefully guiding the AI]"

Degenerates to:

"This button is not working, please fix. I don't care about code. Decide yourself"

Degenerates to:

"Claude make me money"

He says that taste doesn't matter and it hasn't in the past. However, in an era of "extruded code product" (by analogy to https://tvtropes.org/pmwiki/pmwiki.php/Main/ExtrudedBookProd... ) automatically generated by the truckload at negligible cost, the differentiator for software developers will necessarily be the ability to create a product that doesn't reek of extruded code product, i.e. the things like quality that he labels taste.

(Whether any one reading this, myself included, survives in the industry long enough to reach the other side of that transition is a different question.)

[EDIT] The reason I use books as an example is that 4.2 million books were published in 2025 (https://ideas.bkconnection.com/10-awful-truths-about-publish...); 3.5m self published (with most likely LLM assisted or wholly generated) and the remainder traditionally published. (That's ~9,600 new self-published books a day.) Who actually still sells enough copies to make money in this paradigm and why offers hints as to where the software industry is likely headed.

> All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.

In ML it was even worse, we had to throw away a decade of experience, made irrelevant by the new approaches. Even the most revered activity - designing new architectures - became too expensive to do in real life. Fine-tuning models is what we do now, prompting and evals. Like 90% of what we used to learn is no longer needed. And yes, LLMs can do most new ML activities too, they just need light supervision. I am sometimes ashamed to admit I have stopped coding 12 months ago and never wrote one more line, that after 35 years of coding manually. But I also think we will never be without LLMs again, so no point in preparing for 2016 in 2026

I really think that this blog is part of the IPO story from Anthropic or OpenAI.

Because the reality is: A generalist can simply not guide the LLMs through the system nor can he/she verify the output. LLMs are force-multipliers and if there is nothing to multiply, it won't work.

As long we don't have AGI, humans will guide AI and if we get AGI, that's not something to even think of anymore. Software engineering was always about automation, if you want to be an artist, you better look for something else.

Same boat here, just a couple years more experience.

Current LLMs are still kind of shit at actually programming so many jobs do still care to have professional programmers. However, I think it's evident that if things stand where they are, employers will care to have far fewer of them, at least of highly paid highly experienced programmers. If this is the state we're in with LLM adoption when they can't help but create the same helper functions 15 times, god knows we're screwed.

So we should probably work on clearing out our debts and figuring out what else we might want to do with our time, I reckon.

I'm still going to try to do a good job. I'm still trying to learn the best effective ways to apply current LLMs (Right now I still prefer to mostly write code myself but have been using LLMs to bang code into shape via iterative code review; this is a way to exploit LLMs to make better code, especially applicable if your velocity was already good.)

> I have no domain expertise that another Sr. engineer steering an LLM cannot match. All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.

Don’t sell yourself short! Taste is not promptable, I suspect good taste is AGI-complete.

Especially in domains like fintech, there is a lot of accumulated wisdom, and that is what you’ll be handsomely paid for (for at least the next couple years :/ )

For example, architectural patterns, when you need bitemporality, immutable logs, CQRS, all these good patterns that can only be learned by owning years of system architecture - none of these feedback loops are in the training set.

And from a product design side, agents will just miss key concepts and you need a few words to prompt a fix - but that might represent a massive tree search optimization, or the agent on many cases would just fail to identify the requirement. These small steers feel small, but by evaporation our work has distilled down to just the extremely high value insights.

METR task time is still at weeks, doubling every 7 months; it’s years (assuming we keep riding this crazy exponential) until you hit multi-year tasks. I don’t see wisdom / Métis being solved in 2027.

All this said - I think it’s important to extrapolate forwards, if the trend continues, this will may all be true in 3-5 years. Now is the time to pre-register what metrics would make you worried, so that you can define your red lines. There will be a rapid consolidation of power and wealth if these tools continue on their existing growth trajectory.

> ”If you don't steer them, they'll hit a circular dependency issue sooner than you think. Will duplicate code. Add unnecessary comments. Mix up pure functions and side-effects. Disregard the principles of SOLID.”

This is one thing I worry about with AI-driven development on large projects. Every time someone comes along to add a feature it’s likely to lead to wheel-reinvention: dropping in a new bunch of AI-generated code rather than specialising, refining, and reusing some existing code. As the years go by this is going to lead to complex, hugely bloated code bases that are only maintainable by AI tools…

I think the one thing the author is underestimating, especially in his "first pillar" is that he is able to coach the models into great results because he already knows the lay of the land. I often make the same mistake. I see people struggle with GenAI, and feel flabbergasted how they succeed to fail, but then if you observe how they work the tool, it is clear they have no idea what to ask or how to evaluate and iterate on the responses.

> All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.

I think the author downplays how much of that knowledge is used on knowing what to zoom in on, what to prompt, or what to look for.

I still struggle to accept this when my colleagues are producing implementations with AI assistance that are consistently broken and don't do what they think they do. As yet I can't square this circle, no one is better at their job than they were before.

I feel that I am faster and better, sure, but trusting self perception would be an absurd thing to do.

I cannot understand the overwhelmingly negative sentiment in this thread. I've used LLMs every day at work for several years now, I am a firm believer they're useful. Never once have I felt that an LLM could do my job without me steering it. I know the project goals, how to structure the code, the right and wrong paths to explore, how to evaluate and test its changes, how to make good engineering decisions, and much more.

LLMs do not think. Someone has to be in the driver's seat. You're fooling yourself if you think that 99% of the population can use an LLM to write software. Or that they even have the desire to. I can cook, but I often go out to eat. I could repair a leaky sink, but a plumber will always do a better job. The specifics of our jobs are constantly changing, but our role in society will remain the same.

One-off scripts can be written with non-technical prompts, but this work was already cheap. To do anything meaningful you need to be able to reason about a hard problem as a whole and at the implementation level simultaneously. Specifying context, tasks, structure and style, data representation and control flow, these things are software engineering. You are simply writing software in natural language. It has never been about the syntax.

Like all my C skills that I spent ten years mastering aren’t that useful either. But being a hard worker, smart, able and eager to learn new things, and able to judge accurately if I am helping people out (boss, coworkers, customers), these are and always have been the keys to my being a good hire. It’s a great world for smart, kind and hard working people. No idea where the LLMs are going but it will be interesting and I will be able to use and explain them in useful ways for people.

I say this every time: if the LLM is saving you on time, its time to rethink how you spend that time which includes reading the code, reviewing documentation of your preferred stack / programming language, re-learning concepts you feel like you've gotten stale on, and just keep your mind growing on these things. Some of the best devs I ever knew before LLMs were always reading official documentation and reviewing existing code.

I think there are a few important factors to consider (I did not go through all the comments btw so apologies in case, there are too many). Historically lower development costs have also expanded the scope and ambition of what gets built. With LLMs acting as assistants and humans focusing more on architecture and decision-making, teams can explore multiple solutions in parallel and tackle projects that were previously too expensive or complex. That may increase, rather than decrease, the need for people who can manage complex domainy.

There are also practical constraints that could slow or reshape this trend: the growing cost of AI infrastructure and data centers, environmental impact, regulation around copyright and training data, and a likely shift toward specialized domain-specific models instead of a few generic ones. On top of that, if LLMs become the new search engine, advertising and commercial incentives may erode their neutrality.

Finally, LLMs depend on a constant supply of high-quality human-generated data, but they are also reducing the production of that data (e.g., the collapse in Stack Overflow activity). We are also in a very unusual market phase, with a handful of companies heavily investing in and cross-subsidizing each other. It is not obvious that the current pace of scaling can continue indefinitely, or that today's AI landscape represents a stable long-term equilibrium.

There are other factors, like adoption of frontiers LLM in the market that is anyway slow, also the impact of AI in the economy that is anyway based on humans that must be controlled (because it has consequences when it comes pensions, consumptions of goods, etc.. )

Sorry: I used LLM to summarise my points to avoid long wall of text :D

I think LLMs are the great filter of software engineers. They can do a lot reasonable well, but they lack the ability to be an overarching architect of complex projects (and it's my conviction that they can never achieve that).

Only the best humans with insight, intuition and pattern recognition and application in non trivial scenarios can fill that gap.

My prediction is that the review / human in the loop part will become much bigger and more discussed.

Current transformer technology will either plateau or eventually we will get to that singularity bracket. (I was a skeptic once but all signs point there)

And this means models will eventually get better.

The main human value will be

- intent (we call the shots of why and what, AI will take care of the how)

- taste (everyone now immediately identifies Claude designed landing pages, they all look the same, taste changes with time, and can’t be predicted)

- supervision, both before and after AGI, to ensure no accidental damage, no misaligned decision drift, or in the unlikely but still statistically possible case of AI going rouge

Anything else (if we don’t plateau) can be eventually achieved.

Having that said, the fact AI can do it, doesn’t mean we’ll want AI to do it.

If there will be enough demand for handmade creations (with the current anti AI sentiment I can see it having an impact at least as similar to organic food) then we have some hope.

In my opinion the LLMs in the "cloud" reached plateau. Still the same probabilistic hallucinations. No idea what they gobbled up. It's a non-reliable circus type of thing. In fact I more and more go directly to Wikipedia or Grokipedia (if you want to know about Odin etc). There is more and more guardrails and talkbacks. They try to make it more human which makes it even more unusable. My guess, more and more marketing people get involved and things turn to unusable shit. I am more optimistic about local llms in a few years where there won't be any guardrails and you will be able to tweak the model how you like and not some marketing specialist at OpenAI or Anthropic.

“We were taught that generalists and specialists will always have their roles. But now the market is shaping everyone into becoming a generalist.”

I see this as a negative, the whole once everyone has everything than everyone has nothing type of argument. The company I work for believes strongly in keeping humans in control and in the loop which is something I’m grateful for but at the same time who knows how long that will last. Companies are starting to get their AI bills and realizing how much this AI usage actually costs so only time will tell but I hope, for the sake of everyone, that those with the knowledge described in this article make effort to keep their brains in shape.

> Nobody needs A or B-grade codebases anymore because they're being made for LLMs, not for humans to read.

But however effective LLM may be now, shouldn't they be even better if trained on and working in really, really good codebases?

Not only SE career. Many careers which rely on static knowledge accumulated over years.

We have a senior guy who is like a walking cybersecurity library - knowing all the protocols, standards, vendors, threats. He is quite desperate atm. There is nothing he can add to what LLM already knows or can research in 30 seconds. He was building his profile for 30+ years.

I am on the side that AI will change everything. The reason why it has not yet is the lack of skills on the market - no AI transformation leaders. My strategy is to be closer to AI - number of nano startups with few people on board building smth AI related is exploding and they need expertise with AI engineering.

I can't write what I really think because my name is attached to my account.

Let me just say AI is not nearly as good as the billions of dollars in marketing spend say.

We are months away from catastrophic bed shitting and the tech industry will pay the piper.

This can surely feel painful, and I feel occasional sense of dread too, but then realize than not a lot have changed for folks working in medium+ size companies. Domain knowledge and tech expertise were never the sole differentiators for me when it comes to working with or even hiring capable software engineers. There's so much more to software engineer's job and ability to produce code / designs was barely 30% of our jobs. There is this interdisciplinary skillset we had to hone, how to work with others, how to understand what they actually want, how to ship things safely and learn from our mistakes and data to understand what to build next. You said you maintain candid relationship with PMs and stakeholders so you're likely underselling the whole scope of work you do day to day beyond immediate technical delivery.

The time ahead may be rough given the transition period we're in but Software Engineering role (or whatever we will call it in 1-2 years) should not go away.

LLMs surely will be able to do most of the individual work Software Engineer needs to do, but blending them all together is a lot harder task. And once we have AI doing that too, well, I believe at that point vast majority of knowledge work can be replaced by AI too and this is not a Software Engineering problem anymore.

Yeah the writing is on the wall. Not just for knowledge work, but for jobs in general, as I've been saying in other comments. The era of wage labour, and this dominant economic system, is coming to an end. There's no way it can coexist with AI, but it also needs to continuously push for better AI, which means there's no stopping it. The only thing to do really is brace for the disruption - which will likely be pretty rough - and hope governments play their role properly to ease the transition.

The bitter lesson is that there is no domain that will be left which AI won't get good at eventually. So really you have two options: if you actually believe the timeline is long you can keep retreating to the sectors that will be taken over last (emotional support nurse etc) or you can just say if you can't beat me join em and try to supercharge your career/project/life with AI now so it improving helps you rather than hurts you.

Yea, I don't know if I fully agree with this. You are not going to be able to have a vibe coder design, develop and implement a system of moderate complexity without blowing everything up. A big part of using applications like Claude and others is having the conceptual understanding and experience with Computer Science concepts. These are things that vibe coders will never have, if they did, they wouldn't be vibe coders.

LLMs mean less devs are needed, not no devs. even after serveral more decades, they'll need steering. I've seen agents stuck chasing one issue, when to me the issue was obvious, but I can see how the model would rule out the obvious easily and move on, but my instinct/experience tells me that's where I need to focus time on. This translates into costly token-waste. Secondly, it isn't simply "quality", the LLM might generate something that's good quality from its perspective, but it simply won't consider things unless it's explicitly told to in excruciating detail, and even then! understanding things from a simian point of view can't be perfected without that simian experience as part of its training. It can come very close but not quite.

Think of it this way, who needs engineering managers, project managers, scrum masters,etc.. if they're employable then surely actual devs that can tell what good architecture is vs bad, good code vs potentially bug code is are also employable.

But the number of devs needed, that demand will obviously decline dramatically. At the same time though, there are other careers that require programming and software dev as part of your skill set. Simply integrating LLM-enabled solutions into real world workflows is a new area that's very young and immature.

Let's not act like we're suddenly in some sort of post-scarcity utopia where all problems are solved by LLMs, where tech can solve problems, there is demand for those who can use technology to do so. However, I see a lot of people attacking the technology and resisting change a lot, and to those I suggest they look up every single technological revolution and see about the fate of such people.

> I still have one pillar standing, though: code quality and software architecture - what's now being reduced to being called "taste".

Genuine question: what exactly is "quality"?

It's something I've been trying to understand for a very long time. It seems like it's entirely contextual, and it has both subjective and objective facets (the latter only for quantifiable things, and still entirely contextual).

Domain knowledge and architectural skills are not gone. I can say even Opus 4.7 and GPT 5.5 get domain-specific stuff wrong. I use both, because when I am not sure I ask both and also check with Gemini. But these days, I ask those even when I am sure - its like I get something confirmed from a peer. And yes, you have to be the gate keeper - the speed breaker in a way - LLMs still lack a lot of context. And even if they get more context, they will end up costing a lot and still have no accountability. In accounting, one wrong entry and the whole system can be seen as "unreliable" - thats why you are needed. The interesting part is "who takes over" - accountants who become coders, or coders who become accountants. And the latter looks more likely, in any profession. And when that happens - the bar will be raised in these other white-collar professions too, just like what happening in tech.

Opus is getting good at architecture - I need lesser "pushbacks" either because I have learnt to say the right thing or it has learnt to do the right thing - I do not know which one.

Agents are getting good but professing they are surpassing you in domain and architectural knowledge with no special prompting is basically self reporting at this point. That could be your job wasn't that complicated or your personal knowledge wasn't that strong, either way, same result.

Don't get me wrong, I am sure we will get to all three of these pillars, probably by next year. I am not naive.

I'm not worried. You cannot hold a machine accountable and there's no way OpenAI, Anthropic, etc. are going to take on that kind of liability if some code resulted in a major outage or a lawsuit. Perhaps that's the signal I'd be looking for: so much confidence in the product that they put their money where their mouth is.

Besides, you can look at the websites/apps/software you use everyday and evaluate whether or not the agentic era has produced better results. Personally, there's still plenty of bugs and annoyances. Banks still using SMS 2FA, library breakages in minor version bumps, inconsistent UIs between web and mobile, etc.

If all that was a hurdle before... because humans, regulations, or something else... then surely these magical machines that can supposedly replace us and do it much faster would've handled it by now? And they wouldn't introduce more bugs[0], would they? ;)

0: https://www.0xsid.com/blog/meta-account-takeover-fiasco

I don’t think that my career has been eroded by even 1%. The areas of erosion listed by the author feel like reverse order to me. The domain is knowing what questions to ask. That’s the last thing to be replaced.

I think that if you are a skilled software engineer and can adapt, you will be amplified in all aspects. If you just like writing artisanal code, you might have a bad time.

What I tell my team to do is to drop using so many cloud saas apps, and build more themselves using LLMs.

I’m not planning on firing people, but I am planning on building more, using more tokens, and less app subscriptions.

One aspect of building that doesn’t erode is human values.

LLMs don’t create software with zero direction and although I do have 12 agents building constantly, I run out of attention to increase that to 100.

> All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.

Is it really though? Access to information is quicker, but you still need to know what ‘good’ looks like to leverage it effectively. I can prompt my way to a medical diagnosis, but I’d still want to run it by a doctor.

There is no way that LLMs can replace human engineers.

They're often wrong, dumb, and would turn any code into an unmaintainable mess in days if left unchecked.

I use LLMs every day because I can do less work at my job, but they suck and I would never use them for a personal project besides for very isolated and self-contained components.

It's a lot of marketing and hype.

Realistically, what should we have done instead? Not invent LLMs? What happens when a couple thousand people invent the next disruptive technology and even more of the population loses their jobs?

It seems like new tech is something most of us have to lie down and accept as the new reality each time it's invented, barring full-scale rioting. Much as with the Cold War.

I started feeling like a factory worker well before LLMs. My reputation and network stopped mattering and it all came down to take this assessment and do this Leetcode to prove you are a good enough replaceable cog. I have about 15 more years before retirement and I doubt there is anything left to look forward to in my career.

So instead of a programmer, you become a software designer. I recently came across the idea of building fantasy for the player (in context of games), but now that I think more about it. Onlyfans, is just that. Advertising, Beauty products, novels, games, TV shows, and so on. You're really just creating / selling a fantasy for vast majority of people. Most people will never lose that 30 lbs, but you can sell them all kinds of products to fuel the fantasy of them losing that weight, being beautiful, rich, healthy and so on. So an LLM replacing the need for you to write every piece of code, is actually kind of freeing. You as a a former programmer, should embrace your new creative role. Writing code, at least for me was always slow and tedious. I just want to be able to express the ideas I have, so LLMs just make it possible to build things I never could otherwise.

I'm probably a good candidate to direct a platoon of agents. I'm a generalist and I think in terms of business requirements. But I'm not sure I want to. Coding captures my imagination, and directing agents just plain doesn't.

Or maybe it's more than that: maybe I'm off-put by people who have no need to be in the immediate AI race spending a lot of money to get ahead without asking what near-term problem they are trying to solve. It's depressing and makes the whole field more depressing. My advice for them (if they cared) would be that soon all this will be even more batteries-included, to where any dunce can dial up a production-ready app with a sentence. There's no need to rush; when it happens you'll be better off not having wasted millions trying to be on the bleeding edge.

I code myself now and have given up on LLMs, no matter what, they eventually make a codebase unmaintainable. The uncertainity of LLM generated code has been screwing up with my peace and guarantee I have when I wrote code myself. LLMs are not AI they are Jack. Jack of all trades, Master of None.

The market still seems to be hot for roles that provide leverage like platform engineers and Staff+ engineers

The problem with “code quality” and LLM’s taking over your first 3 “pillars” is basically that LLM’s don’t care.

I recently had Cursor evaluate a huge code base that we took over. All public stuff, nothing scary security wise, but it was so convoluted that it was taking me forever to find the bugs. It was written by a person, I should add.

I did this in cursor and after one prompt using Plan, it found all the bugs, created a plan to fix them, it looked good, and I had the agent create the fix.

It took 30 minutes.

The client had this project in the hands of another company without ai tools and they couldn’t fix the bugs she told them about.

So my point is, if we are holding on to our jobs for dear life on the basis that “code quality” matters, you might as well kick down the 4th pillar. Like I said, the LLM does not care.

The reason that I’m looking for an out is that it’s turned everyone I work with into imbeciles.

Nobody wants to think anymore. Coworkers are now just intermediaries for their LLMs. Talking to them is just talking to the LLM - sometimes directly copied and pasted, sometimes minimal effort to conceal what they’re doing. It is so disheartening.

And the sad part is, LLMs are incredible and can enable you to do much better work if you can stay in the loop, and stop focusing only on shipping speed. But from what I have observed, very few people care to do this. Who cares about substance when middle management thinks your productivity is 10x?

I’m excited about the genAI future. I’m a software engineer interested in product, user experience, architecture, and entrepreneurship. After 4 years in the industry, mostly within fintech, I have gotten tired of slow organizations, company politics, nontechnical managers doing the decisions etc.

I’ve saved up a couple of months of salary, have a couple of bootstrap ideas that I believe are within reach for me equipped with a coding agent to build. Hosting can be done almost for free. What used to take entire teams and hence millions of dollars to build can now be done a lot cheaper. If I’m lucky one of those ideas can pay my bills soon. If not I’ll go back to consulting for a couple of months.

Engineering hasn't gone away, you're now just directing things at a higher level. You are now a architect & manager (but you're managing agents not people).

Who sometimes has to deep dive & mentor a agent on solving the right problem.

I am mostly worried about the current AI use in management. I’ve met a few with ”AI hubris” making poor managerial decisions that stem from their poor usage of ChatGPT (not understanding the importance of context, model sycophancy, etc).

Reads like “AI is inevitable” propaganda to me

There is an element of human nature that is known as self delusion and it is extremely common. Almost everyone on HN is suffering from a form of self delusion.

Usually when a human self deludes they do it when they're identity is under threat. People would rather hold on to identity then face the truth at the cost of their identity. That is what is going on in almost every HN thread that has to do with this topic.

A good example is religion. Someone who is intelligent, but born into a religion, will have a hard time giving up that religion EVEN when presented with logical/rational/realistic arguments for why that religion is false. They will rationalize the most convenient reasoning to maintain their own identity.

I mean think about it. Even the concept of religion is obviously false. It's not science, it talks about phantasmic beings that OBVIOUSLY don't exist. It's inconsistent among different groups as in there's thousands of religions in the world and nobody thinks the obvious of the fact that if only religion can be correct, then most of the world is fundamentally believing a total lie.

Anyway, the same thing is happening with AI. AI is eroding our identity as software engineers. So you'll see rationalizations in this thread in attempt to protect that identity. The biggest excuse is LLMs are hallucinate and are often wrong and fortunately for humans... this rationalization still works because it's still very true.

However what people are not mentioning is the obvious. People are avoiding it because they are delusional. The topic of this thread is "erosion" of "software engineering career" AND that is utterly true. ADDITIONALLY the error rate of LLMs have been going down. AI in general is improving. The erosion is real and obvious.

But you will see here on this thread that people are not talking about the erosion. They are holding on to the one last rationalization that is a differentiator without ever thinking about how that differentiator is "eroding" even though "erosion" is the LITERAL topic of the conversation.

I think that the domain knowledge still matters: if for nothing else, then at least it can make the communication both with savvy AI tools and savvy humans more effective compared to "outsiders": acquired vocabulary, truly grokked concepts in the field of target expertise etc… -- that all seem like a huge competitive advantage over folks having to learn all that "on the go", constantly struggling to pick the right nomenclature or using wrong or vague terms. It's mostly that domain knowledge what makes experts understand problems faster or at all, even.

AI is beat thought of as an exoskeleton, you'll be at a huge advantage if you learn how to use it properly, and you will, unfortunately fall behind if you don't. I still think we're going to need people who can reason about code, and the amount of code to reason about is exploding in volume. Think of it as doctors having access to better drugs and techniques - they can can cure more illness, but the bar and expectation of what they can do will just raise. And doctors are still well paid, because what they do is important and needs doing well.

It feels like it's time to start turning the screws on regulation of software engineering.

If productivity is really getting better, regulation can force that productivity to go into increasing software quality.

We have all the AI tools you could bare to mention, but we still don't have anyone but programmers shipping things.

Why aren't the designers and PMs shipping things if these tools are so good?

> Sure, humans should steer the agent to prevent spaghetti codebases with circular dependency graphs. We don't want F-rated codebases that are impossible to touch without breaking something. But a C or D? It's now fine. Nobody needs A or B-grade codebases anymore because they're being made for LLMs, not for humans to read.

Your job is now to get the LLMs to write good code the same way you would do it if instead of AI agents, you would be leading a team of humans.

My public blog response to this - and others saying the same thing - https://blog.herlein.com/post/domain-plus-software-superpowe... - Stop mourning the moat you thought you had. You have a much better one. You just need to pick up the new tool and start using it like it belongs in your hand.

10 years of software development is still young and inexperienced.

"they can do that because there's plenty of articles on the web on how that shit works along with all the technical documentation, and we have blog posts explaining how to apply the technical tools to the domain."

Does this imply the future of LLMs is either that they acquire reasoning, or that they (or even engineering itself?) reach a plateau if humans are no longer writing about how to do things because the humans are using AI?

One other thing I find it is bound to happen is that this domain knowledge you speak of is just going to shift towards LLM domain knowledge.

Look at prompt engineering, and how quickly it became a hot thing. Does everyone know to steer their AI well? There's only so much a harness can do for you once you start attempting to one shot with a single sentence of 4 words.

As others said, "write a Rust compiler make no mistakes" can only work if you overfit a harness to that single prompt. Nobody is going to do that.

So the part you mentioned about the knowledge you accumulated around how to know that "trade-offs between implementations" and "idempotency to prevent double-charges" is just moving to the domain of the english language and tokenizers. One could argue here that this is far more interesting as it requires you to explore deeper into how we communicate and describe the world around us. Reminds me of physics and math.

I think there's an optimism lenses to it if you can grasp it as an opportunity rather than an inevitable doomsday apocalypse.

I don't know, I've been interviewing a lot lately, for jobs that are specifically looking for engineers keen with AI. But they still drill me on the technical stuff, whether I really have the years of experience I claim.

I guess they don't want just any script kiddie. And that seems very logical to me. So I don't really see the big issue. They still want experts. You just shouldn't do the same stuff you did last 10 years.

>Agents do a really bad job at keeping codebases organized. If you do a disciplined way of development with agents by keeping all Documentation in markdown format, repo structure, Decision records and architecture, they do absolutely organized. Every new module should be documented and the editor configuration and coding patterns can be given as reference. this worked well for me. and it make enhancements, extensions development without any big troubles.

LLMs can synthesize the domain knowledge so long as it's within their training data. At some point, blindly trusting the decisions they make becomes gambling.

It's not just about it taking the technical competence away from our job, it's taken away the joy [1] which I wrote about.

I feel like many of my peers are beating around the bush on this topic and in denial. Even if you accept it can do a large portion of the technical part of our work, we are just supervisors at this point making sure it doesn't do any stupid shit. What is the point? Where is the fun in this? Where is the challenge? At least I have enjoyed building my career over the last 20+ years and building software, but find little joy in the work I'm doing now.

I think we're going to see a massive exodus of folks leaving the profession and a huge mental health crisis, long before the folks working in other sectors realise what's hit them.

[1] https://deanclatworthy.com/2026/02/09/the-joy-of-programming...

This reads like a self thought ecommerce/small company employee finding its place in the tech world. These people were erased first, understandably. I am meeting more and more of the same type of people.

I had a friend in LA who was sure that CSS and HTML were enough for her to be a "Senior frontend developer". This year she moved to Tennessee and is trying to find a rich husband because she can't find a single job.

"Except that nobody cares anymore." Noone (from mid-management) cared about it also before. You hit the deadline, get promoted and leave the technical debt to the next one. Even if you're the one to deal with it, you set up the next project, get the budget, prioritize the issues etc. Not much changed in this regard with LLMs

This post is sad. Hacker news is turning into /cscareerquestions as someone who's watched this for last 15 years it's going downhill.

Do any of us? But I think it's kind of backwards the way it's presented in this article; the raw code part it has down more so than the design sense.

I also would point out that, while this thought has occurred to me about the skills being commoditized, in practice I don't see that everyone's getting the same results from the tools. Not sure what's going on but that's interesting.

I think the author missed the forest for the trees - the domain knowledge is what allowed him to successfully use the AI because he instantly knew what was correct or not.

Constant use of AI will probably erode that knowledge over time just because of not practising it, but successful use in complex domain needs the domain knowledge to steer it away from icebergs or hallucination or model flaws.

This anonymous article is likely more FUD from the AI industry. "Just give up,you can't beat the machine. Please go quietly, we want to take your place and it's easier for everybody if you don't resist because you believe it's pointless"

'Maybe I should consider woodworking' - Fuck off.

I used to be in the "AI will soon do all your thinking for you" camp, but I was overlooking a scenario: sometimes the gap between what you understand and what you're trying to achieve is so wide that no prompt can bridge it. Simply asking "what's the right question to ask?" doesn't feel enough, no matter how advanced LLMs become.

Even without LLMs, it was already getting tougher. There are just way more developers than before.

To me, the game has shifted from just “knowing how to code” to being really good at something specific. General software work is becoming cheaper, while deep knowledge in a niche is still hard to replace.

LLMs are speeding this up, but I don’t think they started it.

I don't believe agents care less about architecture than us. Badly architected code has the same effect on them as on us, namely to slow them down and degrade the quality of their output. Which translates to the same thing as well, loss of revenue.

Coding agents are driving up the value of architectural skills to the detriment of more specialized/technical skills.

That woodworking idea is great. It's just a hobby for me as well but after long days of desk work where moving fast is rewarded, it's great to work slowly on a wood project with my hands. I tried listing a couple things on Facebook marketplace but I had never used it before so they just removed my posts.

A famous AI company valued at over $1 Trillion cannot build a terminal app without screen flicker. They are simply unable, you can borrow money from the future but you can't borrow skill. If your software engineering career had any care for software quality it's impervious to erosion.

I am also feeling anxious. I lucked out by having natural inclination towards software development, career which can provide good upper middle class life to anyone. But I feel like writing is on the wall. If I don’t find a way to pivot to something else, I might experience class migration, but in the opposite direction this time.

The OPs domain/subject matter expertise is the part that should elevate their career. Understanding how large applications are constructed should also remain a pillar.

The coding and debugging part will be GenAI and possibly guardrails (harness engineering) tuned specifically for fintech, which they are also well-suited to implement.

Think of it like this

You’ve already faced this the entire time with… libraries on github.

If employers knew how much you can just use a new standard library, or ask you to “use React”, that’s a lot like asking you to use an LLM to speed things up. You also benefit from the collective wisdom of a lot of people. Do you write assembly or pixel shaders by hand?

Agents may have made 80% of your experience go to $0, but the other 20% is exponentially more valuable now. This outweighs your other losses.

The ability to orchestrate intelligence is a magnificent power that few have, and while barriers to entry will be eroded, it will take time and they won't be eroded fully. This is your edge.

What work remains valuable when implementation becomes cheap? How about moving closer to ownership?

I think that in a product-centric or mission-centric perspective, effective automation is good, because it frees you up to do other important things. E.g., in gardening, time spent weeding, is time not spent surviving slug armageddon.

I read all the posts in this thread - but no one has a good idea to avoid software developer obsolescence. My guess is this profession has 5 more years. It was a good run while it lasted.

All the other white collar workers are in the same boat. A pillar of the economy is going to be destroyed with no obvious replacement in sight.

Woodworking sounds like a solid way to steer a career. Un-impacted by LLMs, or any trend/hyper or that matter. Great for keeping the body active after decades of sitting all day. Your produce (literally) tangible products. And above all: there's always a need for more skilled carpenters.

Author says Claude now one-shots distributed systems bugs that used to take him two days but most top comments here are still playing down frontier model capabilities.

Are we collectively in denial? It's understandable as the craft as we knew it is being disrupted by tools that have improved at an astonishing pace.

Advertisement piece for the IPOs. We get this multiple times daily to pump the stocks and demoralize programmers.

The real shift might be that the value is moving from knowing the answers to knowing which questions to ask. The domain expert who steers the AI well will still have a job, just a different one.

I sense FUD in the OP article towards AI, and we have similar article every week on HN now it's starting to become repetitive and tiresome. As of now this OP article is close to 1000 points and 1000 replies. Apparently this FUD seems to resonate with most people at HN, naturally so.

Ironically the entire blog title is the "human in the loop", is probably the biggest counter argument for this FUD. The AI will never ever be concious and responsible, and to function and govern properly in the universe you need to be concious. AI I repeat will never ever becomes one. Not even in the popular fictional Star Wars movie franchises where you can have cute robots but they all devoid of the conciousness for the ever powerful force. Heck even the clones cannot control and balance the force, the Star Wars ultimate conscience.

>Of course, this is good for brilliant engineers that never had the chance to get deep into the domain and now have better chances at getting a job, but it's also sad to think that other brilliant engineers that spent their lives collecting domain knowledge are now competing on the same lane.

Actually overall it's definitely a very good thing for humanities. For example, currently it's very difficult to become a medical specialist and most medical students just stop at GP. But globally there are severe shortages of medical specialists for example typical cardiologists to patients ratio in developing countries is about 100,000:1, and for neurologists it's even worst.

Let's say with AI enabled tools and LLM now these GP can upgrade themselves to become cardiologists easier than before. Let's say due to AI/LLM suddenly there's a big jump of the ratio to 10,000:1 or 10x incraese and improvement in the number of cardiologists without degrading much of the quality of services. Imagine if the typical waiting time for important and necessary procedures like angiography now is reduced to merely days or weeks rather than several months or up to a year. Thus naturally the salary of cardiologists will be not be as high as now, but they still will be compensated handsomely. But as humanity do we really care the cardiologists family just live in semi-D landed houses instead of bungalows, not much me think.

Isn't the solution to learn business skills?

My challenge is seeking good resources for the business skills. I'm doing sales for a passion project for the first time, and it's teaching me a lot. I'm just confused still on why it feels so hard and why I can't find an easier way.

I wonder how do people use LLMs so it does not hallucinates. Like 90% of the time the code is impeccable, but the remaining 10%... Let's say I determine the expertise by how well do people act of these 10%. For me, the first pillar is still there, but not in a good condition

I think we are all vulnerable and need to reassess what it is we bring.

Agents merely accelerate and equalize the playing field. And they cost money. We might be a dying breed, but we are the best operators of this technology. And if we want it, this is our moment.

Yes, get into wood working.

Coding taste and good architecture are the final pillars because AIs are trained on a ton of bad examples that are presented as good examples. That pillar will stand until AIs are able to reconsider and re-evaluate the material they've been trained on.

So are my DSP/Audio ML career, I spent decades acquiring the expertise. Now at 40, I don't know what to do if I lost my current job. Very sad, but from economic POV, I also don't need junior engineers in my team anymore.

Well, not a single 20th-century science fiction novel features programmers; instead, there are platonologists, biologists, and linguists. Humanity is twenty years behind in development because the previous twenty years were spent solely on e-commerce.

While LLM's are beyond junior level at this point, they're still just that. I don't really agree that the first two pillars have been affected.

I've shared a story before that between now and 2 years ago a developer who solely relied on AI has produced the same hot garbage instancing system within the same time period. For example back in my day in 2 years I went from writing a system that struggled with few hundred players to one that could handle thousands and far beyond that. The person using AI 2 years ago wrote a system that didn't work and wrote a system 3 months ago that doesn't work.

Everyone is saying how great AI is, but they're missing that the driver is just as important AI wouldn't be able to achieve any of this without capable (often seniors) using it and giving it guidance. It's really a difference between "it works" and "it works without flaws".

Of course AI can produce things that also "work without flaws" with solved problems and someone "recreating" something that already exists with AI is not that special, a junior developer could accomplish the same thing given the time.

But I do agree that AI becoming part of performance reviews and all that is producing more productive developers which is going to drive the cost way down. In a way AI is stealing from a developers salary and giving it to the AI companies which is pretty ironic considering how cold developers seem towards artists.

I am kind of like of in the same place though roughly 5 years more than author.

I thought about going back to college, learning Math, Statistics, advanced Machine Learning and applying for research role at a frontier lab.

That's a super silly take. As much as I did math and even course on machine learning back in the days and I was making basic perceptron in code at university - to get back and be able to do so on frontier level that's years I don't have anymore.

Anthropic is doing all that also with their LLMs so that ship sailed.

Big thing is — business people are not going to spend time prompting LLM to make an application. If they do then they will become "programmers" and we all (experienced developers) know — you touch it you own it — they (business) will not bother running or taking responsibility.

Right now on r/sysadmin there was bunch of posts where admins have "vibe coded apps" requested to be "productionized". Those business types requesting don't know yet — you touch it you own it — they think they can vibe code app drop it at ops and it is all fun and games. When people will start requesting features, start nagging about bugs, start cursing on whatever changes they introduced it will be back to "hey maybe we will just get someone to do that for us".

  You might not need as deep software dev knowledge but with deep software dev knowledge you still will be faster operating LLM to build systems than non-dev

Even if the model can replace a domain expert on the software side, you still need a human who can decide if the technical solution actually meets the business needs and that would require a human with domain expertise.

I think this is our one chance to build a post-scarcity society. If we fail at this now, things are likely to get pretty bad, economically speaking. For everyone, not just software engineering.

I’m just continuing to get paid as long as I can, while also going back to school part time to train in a role that’s insulated from AI. Having a backup plan at least makes me feel better day to day.

It's not just our careers. In the hunks versus nerds wars, it is now clear who has won. The nerds have made themselves obsolete and put the continued evolution of homo sapiens to an abrupt halt.

LLM is a powerful tool but it still doesn’t have the context that a person would have. A million tokens is a drop in the bucket compared to the overall context that the person guiding the LLM needs to keep it on track and being productive.

If you’re not a good engineer and you don’t have the domain knowledge, your token costs will be very high for whatever gets shipped, because you won’t be able to provide the context necessary to prompt machine efficiently.

Claude will still very often hallucinate bugs, explanations, domain requirements, that have no basis in reality. It will offer fixes and improvements that are pretty standard but not optimal. This is correctable if you catch it, but you need to review every line of code and comment, because in addition to being obviously wrong, it is often very subtle in the wrongness. For every bit of “slop” there is almost microslop, the places where it just kind of confidently guesses… and doesn’t tell you… but sometimes is correct anyway.

The “problem” is there’s less low hanging fruit. You have to know a lot to add value beyond being a middleman gating the slop. You have to really pay attention to the details to find some of the errors that it’s making.

This was a good summary. I feel similar. At this point I think 95% of the skills I've developed over the 2 decades are basically useless. Prior to 2023 I felt like every new skill only made me more employable, but now I don't really see any software skills that are safe from AI today. Even the ones that are very likely won't be in a year or two so there's no point in learning.

I've said this in other threads, but it concerns me how little the average person is preparing for what's coming right now... It seems people are making decisions as if their jobs and income are safe when in reality their entire profession could be gone in less than a decade. People in this comment thread saying crap like "yea, but the code LLMs write still isn't that good by my standards" are totally missing the trend. The fact LLMs are even one-shotting extremely technically difficult problems was something almost no one thought they'd be able to do by now a couple of years ago. Even I as someone who pushed back against this and thought they would become extremely competent within years am genuinely amazed at just how good they are. Trust me, regardless of your opinions, your job and career is at risk.

Another thing to understand is that if AI replaces workers in a variety of fields from SWE, accounting, customer support, graphic design, etc. Then it's likely going to be hard to fine other jobs to pivot into because when unemployment increases that significantly everyone will competing for the same limited number of jobs. Some will fine something, but most will struggle to find anything.

I hear a lot of people talking about how they'll just go into 'x' field if AI comes for their job, but realistically you'll need years of reskilling and you're assuming that in a world where other people are also losing their jobs, and where AI is touching ever more forms of work, that you'll easily be able to get a job in that other field. And I'm not saying that won't happen, just that this isn't as realistic or as safe of a bet as some people seem to think it is. You're also likely deluded about how hard it is to find work because you've been in software for the last decade.

Please, please, please, start preparing for what's coming. The economy is going to get extremely rough over the next 10 years. You need to be prepared to be without income for years, if not indefinitely.

Jobs have always been a bad deal, especially for most people. Very unfair. Less unfair is entrepreneurship. LLMs (VLMs) etc. should make that more feasible for a broader range of people.

Software engineers are fungible commodities, in the wake of the LLM.

Yeah my 10 years experience with Python and Django just flushed down the toilet with the advance of AI, struggling to find a job for a few months now sadly :(

Yes, Code Monkey jobs are gone... I can assure you though that there are plenty of hard problems that reduce human suffering which still need humans to solve them.

The direction I'm given is to take humans out of the loop. As much as possible. Everything AI. Automate everything. If you are in the loop you are overhead.

If the majority of the people have selected a direction you either opt in or opt out.

That's the hard truth.

Governments do dot care on our future, only on who pays them. This is the tragedy.

People are missing the long-term horizon on this. Yes, definitely, you can automate most of your workflows as a software engineer with today's LLM frontier capabilities fully E2E. But many things are still super open: -First, cost is not a settled topic yet. We have no indication that automating everything E2E will be a cost-effective way of doing stuff. So the bare minimum is that you will need some expert designing the workflows in a token-efficient way. Worst-case scenario, tokens become super expensive and only certain parts of the job can be efficiently automated and many companies are not even able to afford tokens. -Second, the system you just "created" is just a static snapshot of today. Yeah it may work fully automated for 6 months, maybe a year. What then? Breaking changes? Updates? Re-designs? What if the quality slowly degrades until nothing ever works again? Who will fix that? There are so many unknowns that it is borderline irresponsible to make guesses on what can be automated sustainably long-term or not. Unless you are OpenAI's Codex team wasting a billion tokens a day on automating and self-improving everything, there is a high chance that everything you set up today is completely useless in a year. -Third, the core engineering workflow hasn't changed a single bit. People like stakeholders, product owners, PMs, etc. can come up with ideas and things to build but someone needs to take decisions on what gets built and what doesn't, balance out paying down technical debt vs. feature development, incorporate new domain knowledge into the system (Or would you expect your PM to be tweaking the prompts about a new regulation regarding GDPR or a completely new legal framework that changes the whole thing?) -Fourth, probably the most important one. If you think AI will soon get good enough to get self-improving and self-sustaining enough to replace full engineering departments E2E with no supervision then nothing else matters because we will all end up without a job and living on UBI (not only tech people). So why do you even care? If it happens it doesn't matter, and if it doesn't happen we just continue doing what we were doing until now. Why do you care?

Use the LLMs to improve your career as software engineer

> I'm just another off-the-shelf engineer now

You're wrong there. You are capable of judging the outcome of the llm.

> But I don't know what to think about the long-term.

Don't you think it all has taken long enough. When I look back at the beginning of my career and compare what we do now ... I cannot shake the feeling we're essentially still solving he same problems and we have accepted that as being normal. Complexity skyrocketed, (abstraction) layers got added but the needle didn't move exponentially together with that. I think the IT industry as a whole gets what it deserves, thinking that we would remain the maze masters of the mazes we create.

> Maybe I should consider transforming my woodworking hobby into a profession...

I'm looking for 8 (affordable) oak panel doors with the exact same measurements as my current doors so I can replace them. That shouldn't be too hard to find you'd think right?

The company is hiring; the author mentioned they are talented and unemployed for the past 8 months. Why not remind the company to re-hire them?

> when I step outside my area of deep knowledge I can no longer call BS on the agents

It's still funny that 4 years into this mania the models can hallucinate basic ground truths, humans are increasingly not reviewing the output, and misusing LLMs where simple automation would suffice.

My wife does project management and works with a lot of tech leads. They came to her with a project plan deck, and she started questioning some weird dates.

The LLM was able to pull artifacts out of their issuer tracker, but it just.. hallucinated some of the dates in the process of creating a project plan deck out of the underlying data. These guys didn't care to review and notice, and who knows what else it hallucinated content wise. They were happy to send this project plan multiple levels up the food chain with hallucinated unreviewed dates.

5 years ago they would have just written a script and had none of this mess.

This person was hired, from the beginning, to be a meat shield. To be responsible for decisions they won't be allowed to make.

I think this experience is universal. The answer is the same as always has been - develop skills that are becoming most important. Right now that is (at least from what I can see): - Data analysis, data pipelines, models, etc. - Tacit business knowledge - architecture and design patterns (always has been, but now the scale os larger so this is even more important)

It's harsh but nobody cares if a model or a human made a system.

The "good" bits are that now automating anything and providing value from software is much easier. If I have an idea or a nitpick somewhere, I can just do it, up to a limit (which is quickly rising).

I have always been a generalist and generally interested in a very wide array of things, and this period has been the most exciting in my engineering career (13y now). Learning about anything is so frictionless, looking back at my first learning experience - picking up a fat C++ book and spending days/weeks debugging, while I can romanticize that, I would never go back.

I can also now write software solo or with an extremely small team at a huge scale in comparison, and that is super exciting.

A lot of skills that took sleepless nights to acquire, they are "gone", but I still don't regret anything or wouldn't go back. Their "usefulness" has degraded, true, but this has always been the case with engineering.

We are now able to spend much more time thinking about utility rather than low level implementation and imo that's great.

We have many challenges ahead of us, and there are seriously bad things, the biggest one I have experienced is the hours are increasing and mental load is vastly increasing as well. As capacity, speed and leverage increases, so do expectations and hours, and that is probably a social problem.

Sorry for the unstructured stream of thoughts, and this is just an opinion (quite an unpopular one I believe), I hope your distress decays away for a new excitement and new opportunities.

Thanks for the article .

Just want to point out that code quality and architecture is actually eroded by codes 5.5. It’s over for this job I think.

I don't care as much about LLMs deprecating my accumulated knowledge. After a quarter of a century in the industry I have plenty of stuff in my head of purely sentimental value.

But for me, the real catastrophe is that they took away all my motivation to learn which was the main work driver for me. Anything I can learn now, the models probably already know or will learn soon enough. Steering LLMs isn't anywhere near being a "deep" skill I'm used to having and it too is being eaten by the agentic tools faster than we learn it. The "make it good" button is coming. And I hate it.

Every 3 lines coming out of Claude has got a bug in it.

There is going to be a lot of demand for people to clean it up.

I've wondered about this a lot. I am brand new to software engineering, fully powered by AI coding. Traditional software engineers have to pivot hard or the are going to be left in the dust. The slow, methodical, take two days to put a change on a production site approach are over. I'm shipping exponentially faster than a co-worker who hasn't embraced AI yet.

> And we all know the demand is drying up.

I don’t think the data really supports this? Last I checked at least.

looks like you just need a bit of harness for your AI: https://leestack.dev/writing/nasa-rules-for-code-that-cant-f...

I don't know how else to say this but LLMs are just word calculators. They are becoming better for sure but at this point even Claude 4.8 is absolute shit at any complex task in a not so common field. I have been working on terrain contour matching algorithms for the last few days and, oh boy are the predictions about AI taking over the world wrong. Its the highest level of bullshit I have ever come across in my life. I ended up writing 100% of the actual algorithm myself. It's a productivity mess.

Software engineers with low self-esteem who built their entire identity as mechanical cognitive workers are having an identity crisis and spreading FUD.

Currently, LLMs are nothing more than amplification tools that require significant steering. If you think your job is mainly to take input from POs or managers, translate it into if/else statements and loops, and review PRs, then you never really understood your role. Software engineering—for those who went to university and studied it—is fundamentally about complexity management and cognitive automation. People in the field, or at least those with some math background who studied software engineering properly, understand that it's all about managing complexity; current tools are nowhere near replacing a software engineer. What they call "taste" is imagination, creativity, embodiment, a more intuitive understanding of context, and yes, superior intelligence compared to current AI. However, AI and LLMs are excellent at mechanical work and mimicking human intelligence, so use them for what they are, and stop whining.

Going forward, the world is ever-growing in complexity, and automation will become widespread everywhere. LLMs just unlocked another level. So basically, cognitive work will be automated—perhaps up to 90%—until the next breakthrough (if ever). You can sit and cry, or you can learn the tools and help shape the future.

Software engineers can automate the entire economy now, including the executives, yet they just sit there whining and crying. This is a self-esteem, confidence, and identity issue more than anything else.

My job as a staff engineer has turned into just reviewing slop farm vomit from offshore devs in Pakistan making pennies on the dollar given a slop code subscription and going wild.

I’ve lately just turned to having Claude do a quick /review, spot checking it, doing my own review and the. firing up some web agents to make the needed changes and just ignoring the back and forth because they don’t give a fuck anyway.

Just waiting for someone to notice and ask the obvious question at this point.

This is good. We want less barrier to entry and more competition in software.

LLMs are good at general solutions but not specific solutions. As industries evolve and laws, regulations, and practices change LLMs will struggle because those things are not included in its training set yet. We'll always need humans to push companies in new directions in order to compete, unless we eradicate capitalism altogether and then we're all out of luck. No competition means no incentive to try and be better than the next guy which means no new products and services for humans to develop that AI hasn't seen already.

I was pretty worried until my company started letting designers and PMs contribute code. They can barely get Codex to do anything. The few who are actually contributing meaningful changes have a SWE background, and they are still doing a pretty bad job at it overall

The glory days are over. In the future, one software engineer will be able to support multiple product areas much like how one HR team can support 1,000's of employees.

LLMs have made domain knowledge and reasoning "cheap"; it doesn't matter if the output is lower quality - look around you for countless examples of where cheap wins and "cheap" continues to improve.

Good luck out there; we will all need it.

> And then I started realizing: all the knowledge I have accumulated over the years: the trade-offs between implementations, how acquiring works, how to structure idempotency to prevent double-charges, everything, was becoming useless.

It’s not useless, at least not yet. And the fact that you recognize this puts you way ahead of the typical HN user constantly crying about how AI could never

What’s going to make you a good AI-augmented engineer is going to be treating AI like a good partner

Not like a genius, not like an idiot - these are extremes where all the memes on LinkedIn are generated

Like any partnership you will see it comes with bad ideas and good ideas - that it will challenge your own ideas and be sometimes wrong and sometimes right

Approaching it this way, I think my learnings only accelerated - the conversation is of much higher value because it’s a fast back and forth where I can take a moment to learn on those occasions where its ideas beat mine

You are feeling a little insecure, paranoid is not the word, and that’s a good thing

Tackle the problem for what it is: I have this sidekick now that can help me bang shit out in a fraction of the time it used to

Use the the brain that got you here to figure that out - don’t waste your time on these debating whether ai is good or not or listening to stories about how it’s stupid because one time it suggested something that wrong

You’re going to be fine, put AI to work for you

Ask me again in a few months but for now you’re fine

> Of course, I'm still employable because someone has to review the code and steer the robot. But I'm just another off-the-shelf engineer now. I have no domain expertise that another Sr. engineer steering an LLM cannot match. All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.

Ownership and responsibility are the new currency for the engineering staff. Willingness to implement these tools and then own the consequences of their use is what leadership is looking for. They want their cake while they eat cake, and they will keep those around who enable something approaching that experience. Owning the side effects of LLM use is more challenging than our own natural output because of the radical volume increase and unfamiliarity with low level details. However, I argue it is still possible. It has always been significantly more expedient to poke holes in someone (something) else's work than it is to perform that same work. And, the executives know this. They leverage this capability too.

The relationship between the business and the development team has been tenuous at best. I've rarely seen a technology team that was properly subservient to the business that ultimately signed their paychecks. I every case I have personally experienced, it is was like a hostage situation where the business owners are in constant terror of the technology people screwing them over in some infinitely nuanced way they or their lawyers could never understand. Many business owners are looking at this technology as a way out of the hostage situation. They noticed a window that was left unlocked. They are going for it right now. Whether or not they will succeed in their escape is a separate matter. Whether or not them being held hostage was justified is also a separate matter. It really helps to keep these things in their own lanes.

Anyone have a copy? Author seems to have deleted it

Software engineers really need to unionise. ASAP.

I'm thinking about taking a plumbing course.

Am I the only one that has noticed the massive increase in buggy software across almost every domain? Like, EVERYTHING has so many more bugs now. Things just break constantly. AI isn’t one shotting fixing bugs, it’s one shotting making hundreds of new ones every time it writes anything.

“even though you're delivering code at a good pace, you're taking too long to deliver those Design Docs. Are you using AI? You should use more AI.”

This here is the crux of it I think… it’s often promoted that AI will give us the time to do the “real” engineering work of designing systems and really serving the user, but in practice all I’ve seen is further attempts at optimizing every last process with AI - just homogenizing every product and feature into slop.

It feels like every leader has been to some talking points boot camp where they’re incentivized to apply pressure to every part of their process - sort of a desperate attempt to justify the costs they’re incurring. I think we will look back at this and see how obviously short sighted it was.

We are finally getting past the denial phase

> Maybe I should consider transforming my woodworking hobby into a profession...

Yeah. There is no future in IT any more, let's be real. Enough CEOs have drunk so much AI kool-aid that they'll lay off so many people it will become outright impossible to get re-hired again when the incompetent CEOs have gotten fired - too much competition.

The only industry that's going to give reliable employment in the future is the trades, especially the regulated/licensed ones. Gas, water, electricity, structural engineers - basically everything where there is actual human lives on the line when things go south.

Start advocating for a wealth tax

don't worry, soon there will be no "software engineering" careers anymore.

So blog with single post hyping LLMs. Oh and the domain name "human-in-the-loop". Call me suspicious.

Hear me out: what about just refusing to use them?

why would i ever want to use a tool that remove the part of my job that brings me joy? Fuck productivity, we were already doing good, when we were able to actually do our job, i.e.: not wasting hours in useless meetings, or doing customer care to idiots who could not be bothered to follow instructions, which i shouldn't be doing in the first place. let the LLM do that, or let the human assisted by the LLM do that. Not my job.

> We were taught that generalists and specialists will always have their roles. But now the market is shaping everyone into becoming a generalist. That's not a bad thing per se, until you look under the economics of supply and demand: if everyone is a generalist, the price of a generalist falls if there's no demand to match. And we all know the demand is drying up.

I see many posts like this every month, and have many conversations with friends and colleagues who think the same, and I see two trends - like this author, they all joke about becoming carpenters, or electricians. Overwhelmingly, electricians. These two professions seem to be held in special regard by software engineers craving the simple life.

I hate to burst yet another bubble, but supply and demand economics apply here too folks. We can't all become electricians and carpenters. The work isn't there. In fact there's gonna be less work there than there is now of the economy tanks and nobody can afford to build or even renovate a house anymore

Contrary to other comments, and this post, I am actually seeing even that third pillar torn down. I have an architect who is very good at what he does, who wrote specs for the AI to follow, and other than a couple awkward placements, all the file structure is very sane and maintainable.

We are in trouble. Not from a "no more devs" side, but from a "we only need one of you 7 to remain .." side

The title should be: LLMs are changing my software engineering career and I know what I should do

Maybe just maybe here in HN we are in an echo chamber that is convincing us that there is a theoretical limit to how far the LLMs can make progress. It’s not unthinkable that LLMs will make better overall architectural decisions or follow the good practices better or understand the problem in bigger picture (more access to company/product context already makes a huge difference)

Lots of jobs have been automated away and careers based on those jobs faded away in history. Maybe in near future there won’t be a ton of opportunities for software engineers in the traditional form. I’m also embracing for that future.

There were people called calculators that did manual calculations in the past. There were people hand weaving all the fabric. There were people painting cars in the factory. All those jobs are gone for the most part.

We are sitting here portending there is going to be demand for software engineers managing those engineer robots but let’s be real. The demand for software is not increasing at the rate software engineering is becoming efficient using those robots. Some (many) of us have to find new careers.

I have no idea what you guys are up to, but it is just a job, it is just a role, it says nothing about you or who you are and it is not tied to your meaning. If you make it so and your perception is aligned with that, then you are not in control of what happens to you. What kind of slavery it is to give other people so much control over you is crazy

Yes this has been my experience as well.

It's crazy the crazed anti-AI people yelling with foam with their mouth that it's useless, meanwhile Claude for me at work oneshots complex bugs in a massive project with a 95% success rate. And the customer happiness survey has never been as good as it's now btw

Become a soldier

move yourself to regenerative ag. take a look.

> I have no domain expertise that another Sr. engineer steering an LLM cannot match.

Sounds like the Dunning-Kruger effect to me. "I can do this with LLMs, therefore any Sr. engineer can do this with LLMs"

You know how we say "adapt or get left behind"?

This is what getting left behind looks like.

Get your thumb out of your ass and learn the damn tools.

> But now the market is shaping everyone into becoming a generalist.

This is interesting because in my field of VC everyone says generalists are dying.

The issue is that the people evaluating you don't know the difference between legit domain expertise and pure bullshit.

AI slop. Prompter probably prompted "don't make it sound like AI slop". But one can still make it out.

computers are made for automation. programmers were always working on automating things, making other things obsolete, and we have been killing jobs for decades. did you really think we would suddenly stop when it's your job? i'm happy this is happening, genuinely giddy

These are the last days of software. Use the AI's and build cool shit NOW.

I don't see it as negatively, in that there are specific trade-offs.

For one: LLMs make a lot of mistakes. We all see that when they hallucinate search results and what not. But, possibly even more important than that, you ultimately become dependent on some big company via LLMs. Perhaps that trade-off is worth it for some companies, but I personally don't want to become dependent on these companies. I actually consider it a hostile attack from the USA, and under Trump this is even more obvious.

Another thing that sucks by LLMs is documentation. They generate a lot of crap that is useless. So that's another area where humans could be better.

Admittedly a lot of vibe-coded AI slop is also useful in some ways, but it has started to make me rather angry in general - youtube already spoiled me here. I no longer want to see ANY AI videos at all whatsoever. It just wastes my time. I am not here to empower skynet version 20.2.

"Except that nobody cares anymore."

:-(

find another job but software engineering :) simple

I don't get those depressing articles. If anything I've been working way more with LLMs and working harder.

I love that Codex added "Steer" to the chat, because I fucking pay attention to the conversation traces on coding agents and I was tired of the PANIC ESC - "No, that's not what you should do! We use a visitor to calculate all that stuff, I just need you to implement another visit method for this new Stuff according to the rules".

Nothing says you shouldn't write any code, in fact, I think that starting from a n interface and a few clients is superior to simply describing things only in natural language. Lots of refactors like extracting method, extracting interface, renaming symbol are way faster, cheaper (no tokens) and less error prone using the IDE.

When I am not sure about the concrete design to implement something, I talk to the coding agent, we discuss types, i suggest patterns, I wrote a small stub here and there, maybe a couple unit tests, but I like to activelly engage with the coding agent as if I was pair programming.

Yeah, I am not a fan of fire and forget, I see no point in being able to control agents remotelly, but nobody is complaining I am not fast enough. It is perfectly possible to have the huge productivity gains coding agents give you without entering vegetative programmer mode. You just need to engage yourself in the process.

You can describe the different categories of something, or you can go ahead and create an enum in rust, you can create a pydantic validator, a few tests here and there. The agent now has something more concrete than a natural language description, it has the compiler to check his code, it has the unit test. The flow becomes faster.

You can mention in your prompt that the agent should use AWS SDK v2 in your Go service, or you can go ahead and add the imports and the initialization somewhere in your code, the second option is a far stronger nudge towards the right direction.

The time you used to waste trying to shoehorn some stack overflow answer to your problem, now you can use to actually read the documentation of whatever you're trying to use. Go ahead, read it fully, you now have time to understand it deeply. The grunt work, the toil will be taken care of by the agent in a few minutes when you're finally feel yourself ready to move to implementation, because now you have the deep knowledge to take the best possible decisions.

There are plenty of places for you to apply your knowledge: the agent may write a correct function, but then this function does a remote HTTP call in the context of a database transaction, so, what happens when the remote http endpoint has a spike on latency? And what if the tables involved on this transaction are a hotspot in your application? You can't add all those small details in your context all the time, you can't add all single corner case and potential pitfall to your AGENTS.md.

Of course, you have to up your game. If all you ever did was completing JIRA tickets without thinking much about it, yes, there's no much you can add to the process beyond what your coding agent can do. But this is a choice.

We can either create a humongous era of slop and technical debt with coding agents, or we can use its hability to free ourselves from toil so we can finally improve our code in correctness, performance, efficiency, efficacy, security and compliance all the while keeping the business happy.

We can either use LLMs to have tens, hundreds, thousands of THERAC-25, or we can use them to liberate our time so we can do the deep work that ensures that you can't possibly deploy a THERAC-25 in production.

Of the posts I've seen by senior devs who assess recent events and end up roughly here:

> I'm still employed and I see myself employed for a foreseeable future. But I don't know what to think about the long-term ... Maybe I should consider transforming my woodworking hobby into a profession.

This one is notable for having all the clues pointing to why that's not the end-state this is headed toward, and yet... still not quite see it.

> I have no domain expertise that another Sr. engineer steering an LLM cannot match.

It's clear he's developed a significant competence in "steering an LLM" but the depth and value of that aren't apparent yet. After ~70 years, software development is now in the early stages of its first tectonic disruption. In the moment, these kinds of tech disruptions mostly appear to be displacing jobs but, historically, we understand the displacement is one part of a larger shift that's vertically compressing roles, functions and labor value. One steam shovel doesn't just displace dozens of pick-axe swinging diggers, it changes the roles, functions and competencies required across the entire supervision and management stack of "make tunnel through mountain" from the crew bosses and site managers to the tunnel engineers and business owners.

The author seems to be successfully navigating this shift but is still mid-disruption, so he and his management aren't yet able to see all the new competencies required or appreciate their value because it's all so new and still evolving. The rapid shock of agentic coding LLMs is especially disorienting because it's the first dramatic disruption in the field.

> review the code and steer the robot.

Historically, it's not surprising those few words are bearing so much weight and unappreciated value. Steam power was a similar shock to every field which relied on earth-moving and shaping. The big machines were quickly deployed, but it took quite a while for all the disruptions to both new and existing roles, functions and necessary competencies to be understood and appropriately valued. I imagine some top pick-axe swingers who'd graduated to being crew bosses and site foremen ended up driving or directing early steam shovels. In the first months they probably had little appreciation for the tremendous amounts of tacit new knowledge and practical expertise they accrued while keeping the steel beasts working. They were too busy being both amazed at the sheer power and frustrated by the constant scalding burns, tip-overs, blown boilers, landslides (too much weight, too little support) and cave-ins (dug too much tunnel, too fast with too little scaffolding), etc.

A big difference in the analogy is the first 100,000 steam shovels weren't sold at ~1/10th their actual cost and simultaneously delivered to job sites worldwide in six months. Software engineering is also unlike earth-moving and tunnel digging, in that the full costs and consequences aren't as visible or immediate as cave-ins and avalanches. The prices of 'steel beasts' are already going vertical with no end in sight and, over the next 18 months, I suspect "management" is about to gain a more viscerally accurate appreciation of the catastrophic costs of digging 'too much tunnel, too fast' absent the close supervision of highly skilled experts in directing all that newfound power constructively and not destructively. Between the skyrocketing full cost of operation and the consequences of poorly managed, non-expert execution - we'll start to see the broad outlines of the new equilibrium take shape.

In the steam era it over a decade for the ecosystem to understand how to even draw a new org chart accurately, label the boxes and appropriately value proven competency where it mattered. The faster the disruption, the longer it can take for all the pieces to rebalance and stabilize around a new equilibrium. Today, the author doesn't know all that he already knows and doesn't yet have the visibility to see how the new domain competencies he's rapidly accruing are creating a different kind of role that could be even higher value.

I secretly wish LLMs take my job away because I'll get about two years of unprogrammed rest, which I absolutely will not take of my own accord. But it's unlikely to happen.

From someone who thinks there's too much AI doom right now and is a glass half full optimist: If you are a software engineer reading this and panicking, don't. The author only mentions his codified, stable knowledge like you'd get from a distributed systems O'Reilly book.

There's no mention of the functional elements of a software engineering role - incident response, working with auditors to define and maintain controls for internal services, handling escalated account support & fraud, working on DevEx, selling shovels (MCPing your consumer-facing APIs/services), getting on customer calls to help sell your company's X feature, managing people downwards and upwards.

The piece kinda reads like remorse over sunken costs and attachment of knowledge to personality. If you twiddle your thumbs and stay static in your role, you will be replaced. It's the differentiation that sets employees apart. And attaching yourself to functions instead of knowledge is the only way to stay afloat.